Comparative analysis of clinical breakpoints, normalized resistance interpretation and epidemiological cut-offs in interpreting antimicrobial resistance of Escherichia coli isolates originating from poultry in different farm types in Tanzania

Introduction. Existing breakpoint guidelines are not optimal for interpreting antimicrobial resistance (AMR) data from animal studies and low-income countries, and therefore their utility for analysing such data is limited. There is a need to integrate diverse data sets, such as those from low-income populations and animals, to improve data interpretation. Gap statement. There is very limited research on the relative merits of clinical breakpoints, epidemiological cut-offs (ECOFFs) and normalized resistance interpretation (NRI) breakpoints in interpreting microbiological data, particularly in animal studies and studies from low-income countries. Aim. The aim of this study was to compare antimicrobial resistance in Escherichia coli isolates using ECOFFs, CLSI and NRI breakpoints. Methodology. A total of 59 non-repetitive poultry isolates were selected for investigation based on lactose fermentation on MacConkey agar and subsequent identification and confirmation as E. coli using chromogenic agar and uidA PCR. Kirby Bauer disc diffusion was used for susceptibility testing. For each antimicrobial agent, inhibition zone diameters were measured, and ECOFFs, CLSI and NRI bespoke breakpoints were used for resistance interpretation. Results. According to the interpretation of all breakpoints except ECOFFs, tetracycline resistance was significantly higher (TET) (67.8 –69.5 %), than those for ciprofloxacin (CIPRO) (18.6 –32.2 %), imipenem (IMI) (3.4 –35 %) and ceftazidime (CEF) (1.7 –45.8 %). Prevalence estimates of AMR using CLSI and NRI bespoke breakpoints did not differ for CEF (1.7 % CB and 1.7 % COWT), IMI (3.4 % CB and 4.0 % COWT) and TET (67.8 % CB and 69.5 % COWT). However, with ECOFFs, AMR estimates for CEF, IMI and CIP were significantly higher (45.8, 35.6 and 64.4 %, respectively; P<0.05). Across all the three breakpoints, resistance to ciprofloxacin varied significantly (32.2 % CB, 64.4 % ECOFFs and 18.6 % COWT, P<0.05). Conclusion. AMR interpretation is influenced by the breakpoint used, necessitating further standardization, especially for microbiological breakpoints, in order to harmonize outputs. The AMR ECOFF estimates in the present study were significantly higher compared to CLSI and NRI.


DATA SUMMARY INTRODUCTION
Antimicrobial resistance (AMR) has emerged as a major public health concern due to the rapidly diminishing efficacy of antimicrobial therapy [1]. Phenotypic approaches have long been acknowledged as the gold standard for detecting resistance both in clinical and in microbiological contexts [2]. Despite the prominence of molecular approaches, phenotypic techniques remain crucial for quantifying resistance and sensitivity, whereas genotypic techniques are useful for predicting resistance and determining resistance mechanisms [3]. Susceptibility testing is commonly used in clinical settings to determine how bacteria respond to empirical therapy, and one of the most popular criteria for determining if an antimicrobial is effective is estimating the lowest dosage at which microbial growth may be suppressed [3][4][5]. Well-known susceptibility metrics include inhibition zone diameter (IZD) and minimum inhibitory concentration (MIC) [5]. In the MIC assessment, micro-organisms are cultured in liquid media in the case of broth microdilution in slots with varying concentrations of the antimicrobial agent under study, ranging from high to low, and the lowest MIC score inhibiting growth of microbes is then determined [5]. IZDs are usually measured on solid media, in which antimicrobial discs of known concentrations are placed on plates streaked with bacteria culture, the antimicrobials diffuse away from the disc, generating a concentration gradient that inhibits bacterial growth at a measurable radius from the disc, resulting in a zone of inhibition [5,6]. The wider the inhibition zone, the easier it is to treat the population of micro-organisms being examined.
Selecting a breakpoint guideline to adopt is largely driven by the objective of the analysis [7,8]. AMR in clinical and microbiological contexts differs greatly, as do the functions of breakpoints [8]. In clinical settings, the term 'resistance' refers to a condition in which a patient's clinical recovery requirements are not met while receiving the correct antimicrobial dosage [8][9][10]. By contrast, in a microbiological context, resistance pertains to the mechanisms that make an isolate less susceptible to an antimicrobial agent compared to other isolates of the same species [2,10]. Therefore, microbiological breakpoints distinguish isolates that have evolved resistance through mutations or horizontal gene transfer from wild-type isolates, independent of whether the degree of resistance is clinically significant [7]. The current investigation considers wild-type organisms as those with 'typical' susceptibility patterns to antibiotics [11]. These would be regarded as those not having acquired resistance genes or genetic changes, making them sensitive to antimicrobial agents [11]. There is a significant knowledge gap and a lack of understanding of the application of the different breakpoints in the literature. In recent literature, clinical and microbiological breakpoints are often used interchangeably [7,8], leading to confusion and a reduction in the relevance of the research involved. Even though EUCAST (European Committee on Antimicrobial Susceptibility Testing) epidemiological cut-offs (ECOFFs) and CLSI (Clinical & Laboratory Standards Institute) breakpoints strive to strike a balance between clinical relevance (e.g. application of pharmacokinetic/pharmacodynamic principles in establishing the breakpoints) and the need to identify emerging resistance, ECOFFs generally maintain that organisms found in the wild-type distribution (susceptible population) have a low likelihood of clinical treatment failure [12]. This may translate into lower breakpoints for EUCAST ECOFFs compared to CLSI, leading to a broader categorization of isolates as susceptible. Erroneous classification of certain isolates as susceptible based on EUCAST breakpoints could theoretically increase treatment failure rates [13]. Despite lack of consensus in the use of breakpoints in the literature, ECOFFs remain the most widely used breakpoints in microbiological research, while CLSI breakpoints are the standard in clinical settings [7][8][9][10]. EUCAST ECOFFs were developed in Europe [7], while CLSI breakpoints were developed in the USA [7,11], although both breakpoints are now universally acknowledged. Recent research has offered a novel approach for establishing breakpoints that employs normalized resistance interpretations (NRIs) to address the EUCAST ECOFF constraints [12][13][14]. The method involves entering MIC zone sizes of a set of isolates into a spreedsheet and calculating the distribution, smoothing the distribution using rolling means, identifying the peak of the smoothed distribution and calculating the estimated total number of wild-type (WT) observations [12]. The distribution of percentage, cumulative percentage and probit values of the WT observatons are then calculated, and the slope and the intercept of the best-fit line of the probit values versus zone size are determined using a least squares method [12]. The mean and standard deviaton of the normalized WT distribution are then calculated, and the epidemiological cut-off values are set at the mean minus 2.5 times the standard deviation [12]. The functional peak serves as a reference point for determining the portion of the distribution that corresponds to WT isolates and contributes to setting cut-off values/breakpoints for resistance interpretation [12]. A functional peak is generally established after a putative peak has been identified and modulated according to protocol conditions stipulated by Kronvall and Smith [12] using the NRI method. However, one major flaw of this method is that it assumes that the WT observations are symmetrically distributed around the peak which may not hold true in all cases [14][15][16]. Moreover, the accuracy of the NRI method can be influenced by the size of the dataset used for analysis [12,14,16]. Small sample sizes may lead to less precise estimates of the parameters such as the functional peak and standard deviation, potentially affecting the reliability of the interpretation [12,14,16]. However, the generated bespoke breakpoints allow for laboratory-specific strain classifications of WT and non-wild-type (NWT) strains [12][13][14]. The method allows for reconstruction of the normalized peak for MIC or IZD distributions as long as resistance is not developed in the WT population [12,16]. Tiny variations in zone diameters including low-level resistance in the WT populations can, therefore, be identified even within populations considered primarily susceptible by traditional interpretations (EUCAST and CLSI) [12,16]. Furthermore, novel forms of resistance can also be detected, increasing antibiotic susceptibility testing sensitivity and precision [13]. By normalizing the data, the technique improves comparability between laboratories and minimizes the effect of variability on resistance interpretation [13]. Consequently, standardization of breakpoints is possible in certain datasets, provided reproducibility and precision are demonstrated [13,14].
Despite developments in microbiological breakpoints, research into how these breakpoints perform when applied to data from low-income countries is sparse. There is still a dearth of knowledge concerning the validity of prevalence estimates of data from low-income countries employing these breakpoints. EUCAST ECOFFs and CLSI breakpoints are primarily generated with data from developed countries [9]. Moreover, since most of the data used to generate the ECOFF reference distribution are of human origin [9], the underlying breakpoints are unlikely to provide an accurate framework for evaluating data from animals or the environment.
The present study evaluated the prevalence of AMR in four types of poultry farms in Moshi and Arusha, Tanzania, using CLSI breakpoints, ECOFFs, and bespoke thresholds generated from NRIs. The study also aimed to ascertain whether the selected breakpoint has any bearing on resistance prevalence predictions.

Study design and location
This study was part of a broader prospective cross-sectional study conducted in Arusha and Moshi districts in Tanzania whose aim was to determine whether different poultry husbandry systems were associated with varying degrees of AMR among poultry populations. The initial study collected 746 samples out of a target of 800 from four different farm types, with ten cloacal swabs collected per farm type. These data were collected from selected wards, with ten selected from each district, Arusha and Moshi. In the context of this study, wards refer to administrative subdivisions or smaller geographical areas within the districts of Arusha and Moshi, which are used for local governance and representation. Taking budgetary considerations into account, 74 plate sweeps were shipped to the One Health Research in Bacterial Infectious Diseases (ORHBID) laboratory, located at the Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, for analysis. Of the 74 plate sweeps, 74 isolates which exhibited successful growth following subculture on MacConkey agar were selected for the present study, with each isolate representing each plate.

Identification of antimicrobial-resistant lactose-fermenting coliforms on MacConkey agar
Using a modified breakpoint plate method described by Caudell [17], we assessed the total population of coliforms and resistant coliforms present on MacConkey agar with and without antimicrobials [15]. Interpretation of resistant coliforms to the selected antimicrobial drugs at defined concentrations was conducted according to CLSI 2016 guidelines. Coliforms formed pink to red colonies, while other lactose-intolerant Gram-negative bacteria formed pale white colonies. Frozen (−80 °C) cloacal swab samples were thawed overnight at 2 °C for isolation. Following homogenization, 50 µl of each sample was added and vortexed with 450 µl of maximum recovery diluent (MRD; Oxoid Thermofisher). Plating was performed on plain MacConkey plates withiout antibiotics and MacConkey plates supplemented with antimicrobial agents using a spiral plater (Spiral System) programmed to dispense 50 µl of the mixture in a logarithmic dilution. Coliforms were enumerated using the spiral plater grid technique at Kilimanjaro Clinical Research Institute (KCRI) after incubation on plain MacConkey agar and MacConkey agar with antimicrobials. Each plate was mapped with a grid, placed on a level surface and adjusted so that the grid's centre corresponded to the plate's centre on the viewer. Colonies were counted from the outer border of each section into the centre, allowing the bacterial concentration to be estimated.

Collection and storage of plate sweeps
Coliform bacteria plate sweeps were obtained from plain MacConkey agar plates and subsequently preserved at −80 °C. Two vials of plate sweeps were collected from each plate, with both vials subjected to storage in a preservation medium consisting of MRD media and 15 % glycerol. One vial was stored at −80 °C and retained in Tanzania for future reference (archived), while the second vial was temporarily stored at −80 °C, awaiting shipment to the OHRBID laboratory at Glasgow University (aliquot used in the present study). To ensure preservation during transportation, the frozen plate sweeps were shipped using dry ice. The primary objective of this shipment was to facilitate further analysis and investigation at the aforementioned laboratory.

Phenotypic identification of Escherichia coli using chromogenic agar
At the OHRBID laboratory, cloacal swabs were thawed overnight at 2 °C and 50 µl of the sample was homogenized with 450 µl of MRD. The mixture was vortexed, and 50 µl was plated on MacConkey agar with a spiral plater (Spiral System) and incubated at 37 °C. Pink lactose-fermenting colonies were inoculated on Luria-Bertani broth (Oxoid) and incubated at 37 °C for 24 h. Pure culture (50 µl) was inoculated on chromogenic agar (CHROMagar ECC; Sigma Aldrich), spread evenly using a sterile L-shaped spreader (VWR; catalogue number 6121560P) and incubated for 24 h at 37 °C. Phenotypic blue colonies indicated the presence of E. coli isolates, and selected isolates were confirmed via quantitative uidA PCR as described in the section below on comfirmation of E. coli species

Reference strains
As part of this study, reference strains originating from dogs were obtained from the University of Glasgow's Veterinary Diagnostic Services laboratory for subsequent analysis. Identities of the strains were confirmed using API 20E strips (API system by bioMérieux, available at https://www.biomerieux.co.uk/product/apir-id-strip-range). A positive E. coli control and a negative Klebsiella species control were used for both genotypic and phenotypic confirmation of E. coli isolates. These two reference isolates were resistant to all antimicrobial agents used in this study.
Molecular detection of E. coli using quantitative uidA PCR DNA extraction DNA extraction was conducted using a QIAamp DNA mini-Kit (Qiagen). Isolates from CHROMagar were resuspended in 1 ml Luria-Bertani media (VWR) and 50 µl was processed according to the manufacturer's instructions provided with the QIAamp DNA mini-Kit. DNA concentrations were determined using the NanoDrop (NanoDrop-2000 Spectrophotometer; NanoDrop Technologies).

Confirmation of E. coli species using uidA PCR
Real-time quantitative PCR (RTqPCR) was performed using the Rotor gene system (Applied Biosystems) to identify the uidA gene, an 1809 bp gene expressed by all E. coli bacteria. The uidA RTqPCR primers and probe used for detection were as described by Frahm and Obst [16]. The probe was labelled with 56-FAM as a reporter fluorescent dye at the 5′ end and the 3′ end with BHQ_1 as the quencher dye. Reactions for uidA RTqPCR were performed as described by Frahm and Obst [16]. The RTqPCRs were performed in a 15 µl reaction volume using 2× Quantitect Probe PCR master mix (Qiagen), 0.4 µM of each primer, 0.2 µM of probe (Integrated DNA Technology) and 5 µl of template DNA from presumptive E. coli isolates. PCR cycling conditions consisted of an initial denaturation step at 95 °C for 2 min, followed by 45 cycles of denaturation at 95 °C for 5 s and annealing/ extension at 60 °C for 5 s.

Culture and susceptibility testing using disc diffusion test
Antimicrobial susceptibility testing (AST) was conducted using a standardized disc diffusion technique [17]. E. coli was tested against four [4] antimicrobial agents at standard disc quantity according to EUCAST recommendations, i.e. ceftazidime (30 µg), ciprofloxacin (5 µl), imipenem (10 µg) and tetracycline (30 µg). The procedure involved diluting the culture suspension with distilled water to a density of 0.5 MacFarland. Mueller Hinton agar was poured in plates (90 mm in diameter, 4-6 mm in depth). Prior to inoculation, the plates were air-dried for about 30min. Bacterial suspensions at 0.5 MacFarland were streaked evenly across the surface of the medium with a plate spreader (VWR; catalogue number 6121560P). After drying for 3-5 min, the four antimicrobial discs were placed on the agar surface using a sterile forceps and gently pressed down to ensure contact. The plates were incubated at 37 °C under aerobic conditions. After overnight incubation, the zone diameters were measured on the reverse side of the culture plate using a vernier calliper.

Data analysis
The IZD for each antimicrobial agent tested was assessed using breakpoints, CB, ECOFFs or wild-type bespoke cutoff (CO WT ) values based on NRI to identify the prevalence of susceptible and resistant isolates. The clinical breakpoints (CBs) were determined using the 2016 CLSI guideline, ECOFFs were generated using EUCAST guidelines [11]. Since ECOFFs for 30 g tetracycline were unavailable on the EUCAST website, the distribution of tigecycline, a member of the same antimicrobial class as tetracycline with the requisite concentration, was substituted for visual comparison purposes. The focus was on comparing the distribution patterns between the current study and the mentioned antimicrobial class reference distributions, in order to assess the similarity or dissimilarity with our data. However, it is important to note that the breakpoint for tigecycline was not used in this analysis to ensure accuracy and avoid potential misinterpretations. All calculations of the CO WT were conducted according to specifications in a published protocol by Kronvall and Smith [12] using a spreadsheet provided by the authors (European patent No. 1 383 913, US Patent No. 7,465,559; https://doi.org/10.1111/apm.12624). The IZD histograms and CO WT values for each compound were computed using the provided spreadsheet. Additionally, the construction of the functional peak was generated following the steps outlined by Kronvall and Smith [12], utilizing an automated sheet available online at https://doi.org/10.1111/apm.12624.
The prevalence of resistance among poultry E. coli isolates from Tanzania was calculated using three distinct thresholds: CB, ECOFFs and CO WT .

Phenotypic and molecular detection of E. coli
Out of 74 plate sweeps that were shipped to the University of Glasgow for further analysis and subsequently cultured on MacConkey agar, a total of 74 isolates (one isolate per plate sweep) were successfully cultivated. Following subculture on CHROMagar from MacConkey agar, 72 isolates displayed blue colonies, indicating a presumptive identification as E. coli. However, subsequent confirmation using uidA PCR revealed that out of these 72 isolates, 59 were confirmed to be E. coli (data available in Table S1: Molecular_Phenotypic_results, available in the online version of this article).

Susceptibility testing
In line with the objective of the current study, which seeks to compare resistance interpretation based on three breakpoints, we began by visualizing the range of IZD values collected per antimicrobial as seen in Tables 1 and S1: Distributions. For each antimicrobial class, there was at least one isolate that exhibited a zone diameter of 6 mm. Following that, the distribution of IZD values was examined using the NRI approach and visualized across all antimicrobial classes, as illustrated in Fig. 1. The approach allowed the determination of the mean zone size and standard deviation (sd) for WT isolates, as well as CO WT for each compound, as depicted in Table 1 and normalized histograms in Fig. 2. The estimated sd values for ciprofloxacin, imipenem and ceftazidime IZDs exceeded the recommended limit of 4 mm [18], except for tetracycline ( Table 1).

Comparison of EUCAST reference data and Tanzanian poultry data
The distribution of IZD values for E. coli isolates (Table S1: Distributions) obtained from poultry in Tanzania showed a noticeable shift towards lower IZD values across all antimicrobial agents tested. This shift in values, as illustrated in Fig. 1, indicates reduced susceptibility levels compared to the reference data provided by EUCAST. Specifically, the CB for tetracycline was lower than the CO WT . While the clinical breakpoints deviated from the CO WT by 5 mm or less for all compounds, the ECOFFs were significantly higher than the corresponding CO WT values. The wild type cut-offs (CO WT ) were lower than CB and ECOFFs for all antimicrobials except tetracycline (Fig. 1).

Estimation of the prevalence of AMR in E. coli from Tanzanian poultry based on ECOFFs, CB and CO WT
There was no statistically significant difference in the prevalence estimates of ceftazidime, imipenem, ciprofloxacin and tetracycline resistance when interpretation was conducted using the CO WT cut-off and clinical breakpoints (CB) (χ 2 =1.29, d.f.=3, P>0.05). However, when comparison of the interpretations of the ECOFFs with the other two breakpoints was conducted, a significantly higher prevalence of resistance was observed for ECOFF values, except for tetracycline where breakpoint values were unavailable for the desired concentration. This difference was statistically significant, as presented in Table 2 (χ 2 =23.91, d.f.=4, P<0.05). The proportions of susceptible isolates determined by CB, CO WT and ECOFF values were not significantly different (χ 2 =2.830, d.f.=4, P>0.05) for CEF, CIPRO and IMI, as presented in Table 2.

DISCUSSION
The aim of this study was to examine whether AMR estimates varied depending on the AMR breakpoint used. In contrast to clinical breakpoints, which define resistance as the likelihood of treatment failure, epidemiological cut-offs and bespoke normalized resistance interpretive breakpoints use microbiological criteria to define resistance [2,8,10]. Previous studies have used these breakpoints to interpret resistance [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The current study compared prevalence estimates of resistant E. coli isolates based on the epidemiological cut-offs (ECOFFs), CLSI break-points (CBs) and NRI bespoke breakpoints (CO WT ). Prevalence estimates of ceftazidime, imipenem and tetracycline resistance based on CO WT and CB did not differ significantly; however, ECOFF values for ceftazidime and imipenem resistance were significantly higher. Ciprofloxacin resistance varied significantly across all three breakpoints. However, ECOFFs generated the highest prevalence estimates of ciprofloxacin resistance, followed by CB, and the lowest estimates were generated by CO WT . Finding resistance to carbapenem (imipenem), third-generation cephalosporin (ceftazidime) and fluoroquinolone (ciprofloxacin) in poultry is alarming as these antimicrobials are listed as World Health Organisation (WHO) Critically Important Antimicrobials. Third -generation cephalosporins are designated as Highest Priority Critically Important Antimicrobials (HP CIAs) [20]. Additionally, carbapenems and third-generation cephalosporins are rarely used in livestock production in Tanzania [21,22], making their resistance presence a cause for concern.
Clinical breakpoints (CB) and CO WT breakpoints did not differ significantly in their interpretations of resistance to antimicrobial agents, especially for ceftazidime, imipenem and tetracylines, an observation that differs from the observations of Dias et al. [18,23], where AMR prevalence estimates varied according to the thresholds used [18]. NRI may, however, interpret values as susceptible if (low-level) resistance is prevalent in a dataset [13]. Furthermore, the NRI method has a fundamental flaw in that cutoffs generated from small datasets may not be accurate or representative of a larger population [18]. For instance, three antimicrobials in our data exceeded the allowable standard deviation [18], which necessitates caution when interpreting the results of our study. Small datasets are more susceptible to outliers and random variation, which can lead to high sd values and high variability in NRI results [18]. The present study used a small dataset. As a result, meaningful trends and accurate interpretations are constrained. Additonally, there were several distributions in our study that were bimodal rather than unimodal. Since the NRI technique estimates CO WT values based on the distribution's highest peak assuming a normal distribution, the significance of the second peak on lower IZD scores is likely to be overlooked, despite it indicating the presence of an intermediate population.
To fully understand this phenomenon, larger datasets are needed [11,24].
The prevalence of tetracycline resistance based on the current dataset was higher than that of other antimicrobials, while ceftazidime and imipenem resistance were low, and ciprofloxacin resistance was moderate. The results were consistent with previous research conducted in the northern part of Tanzania where similar occurrences of tetracycline resistance were found [21,22,25]. One of the underlying driving factors is the widespread use of tetracycline in poultry production in northern Tanzania  . Considering most farmers in the northern zone of Tanzania use tap water for poultry production, the presence of blaTEM genes and blaCTX-M79 in tap water may explain ceftazidime resistance in E. coli isolates from animals that were not exposed to antimicrobials [26]. Prior to this study, no research into imipenem resistance in poultry had been conducted in Tanzania. As a result, no direct evidence could be found indicating the origins of imipenem resistance in poultry. Despite restrictions on imipenem usage in Tanzania, informal use may occur due to limited regulatory enforcement and access to antibiotics [30]. This informal practice can be driven by factors such as antibiotic availability without a prescription, self-medication culture and economic considerations in the poultry industry [30]. Animals can also acquire imipenem-resistant E. coli from humans via faeces if they are exposed to human excrement [28]. In the current study, poultry-derived isolates had smaller zone sizes than EUCAST reference isolates and were subsequently classified as resistant based on EUCAST thresholds. This highlights the potential for misclassification of a portion of poultry isolates from the normal distribution as resistant, according to the EUCAST reference distributions, and ECOFFS (which are primarily derived from human-centric datasets). Similar shifts have been observed when comparing EUCAST data to Gram-negative isolates from animals [18]. Humans and animals have inherent variability, which may explain why WT distributions considered normal in animals may not be normal in humans. Animals, including ruminants and other herbivores, have more complex digestive tracts than humans [30, 31, 32]. One key factor contributing to this variation is largely due to the diversity of microbes in the animal gut versus that of humans [33,34]. The animal gut contains a wider variety of microbes that contribute to metabolic processes and nutrient breakdown [33,34]. The pH of the gut, nutritional availability, diet and interactions with host factors can influence bacterial proliferation, including those that harbour antibiotic resistance genes [35]. In agricultural and veterinary settings, animals are often exposed to antimicrobial agents for therapeutic puposes, growth promotion or prohylaxis [30]. In the presence of this selective pressure, resistance to antimicrobials, including resistance caused by efflux pumps, can develop and spread. Furthermore, varying antimicrobial use patterns can contribute to differences in resistance profiles, as well as efflux pump expression. According to recent research, upregulated efflux pumps have been found to be prevalent in animals compared to humans [31]. The AcrAB-TolC efflux pump system, for instance, has been linked to multidrug resistance and frequently is upregulated in animal-associated bacteria, such as poultry [36]. On the other hand, the MexXY-OprM efflux pump system, which is frequently found in Pseudomonas aeruginosa, confers resistance to many antimicrobials, including flouroquinolones and aminoglycosides, and has been observed to be highly expressed in animal-associated strains [37]. Contrary to the underlying evidence supporting IZD variation, Sjölund et al. [38] found similar distributions between human and poultry isolates, despite poultry-derived isolates being collected from environments without antimicrobial exposure, thus suggesting that the two populations have similar WT (wild) populations. In contrast to what was found in this study, Sjölund et al. [38] revealed that wild-type bird isolates exhibited similar distributions to human isolates, despite being collected from birds in pristine environments with little exposure to antimicrobial agents [38]. Nevertheless, other reasons that may explain variations and decrepancies may have been attributed to methodology in these studies, despite systematic efforts to standardize procedures [12], Furthermore, EUCAST distributions are inherently known to originate from data generated by different sources [39]. Our observations, however, may be an artefact of the resistance mechanisms that might have developed as a result of antimicrobial exposure, and hence the shift to lower zones of inhibition.
Our study acknowledges the potential bias introduced by selecting microbes based on a single breakpoint at the beginning of our analysis where we implemented a screening approach using McConkey agar with and without antibiotics, categorizing isolates that grew on media with antibiotics as resistant. We recognize that including the three breakpoints would have provided a more comprehensive assessment of the impact of different breakpoints on resistance classification. However, due to technical limitations, we were unable to incorporate that at the beginning of our study. Despite this limitation, our study still provides valuable insights by focusing on the comparison of specific breakpoints and their implications in resistance classification. By investigating these selected breakpoints, we shed light on their specific characteristics and provide meaningful insights within the defined scope of our study

CONCLUSION
This study illustrates how different thresholds can impact the interpretation of resistance. ECOFFs or CB thresholds may overestimate [40] or underestimate prevalence when used instead of bespoke thresholds. EUCAST is largely composed of the humancentric datasets SENTRY and MYSTIC, with little representation of African and animal data. As a consequence, the EUCAST dataset does not accurately portray the WT distribution of human or poultry E. coli isolates from Africa. Additionally, clinical breakpoints are developed for human therapeutic purposes, but the same breakpoints are applied to interpret information from various animal studies. Considering limitations of datasets used to generate the thresholds, it is uncertain whether the existing threshold schemes are true universal reference metrics for resistance interpretation, since they may lead to misinterpretations of resistance, particularly in low-income countries. It is important to re-examine the current thresholds and include data from low-resource countries to make the thresholds more inclusive.

Conflicts of interest
The authors declare no competing interest. The comment of reviewer 2 on the differences between the archived and the stocked samples is still not addressed, and the statement is a bit confusing. Please clarify.

Ethical statement
I have edited the text to make it clearer (L186-L193). I have stated that vials that were archived were the ones retained in Tanzania. Both the vial used in the current study and the one archived were put into tubes with MRD media and 15% glycerol.
Supplementary tables have been provided, but they are not named appropriately following the platform's guidelines. Furthermore, they are not cited in the results section where it could help the reader's understanding. Also, please provide a Comments: Thank you very much for your efforts in including all the reviewers suggestions, the manuscript has undoubtedly improved in quality. However, there some changes that still need to be address: ·Please review the use of italics throughout the manuscript as some are missing (e.g. gene names such as uidA and the et al. abbreviation on the citations need italics) ·The comment of reviewer 2 on the differences between the archived and the stocked samples is still not addressed and the statement is a bit confusing. Please clarify. ·Supplementary tables have been provided, but they are not named appropriately following the platform's guidelines. Furthermore, they are not cited in the results section where it could help the reader's understanding. Also, please provide a We appreciate the reviewer's concern regarding the potential bias introduced by selecting microbes based on only one specific breakpoint at the beginning of our analysis. We acknowledge that including the three breakpoints would have provided a more comprehensive assessment of the impact of different breakpoints on resistance classification. However, due to technical limitations, we did not include that in our study at the beginning. We believe that despite this limitation, our study still contributes valuable insights by focusing on the comparison of specific breakpoints and their implications in resistance classification. While our findings may not capture the full spectrum of resistance profiles across all breakpoints, they shed light on the specific breakpoints investigated and provide meaningful insights within the scope of our study. We have highlighted this limitation in the discussion section to ensure transparency and encourage future research to address this gap by considering a broader range of breakpoints. We hope that our study's contribution in evaluating and comparing the selected breakpoints will be valuable in advancing the understanding of resistance interpretation. We appreciate the reviewer's feedback and will ensure that these limitations and their potential impact are duly acknowledged and discussed in the revised manuscript.
L133: Please clarify where each experiment was performed.-I have now clarified this L137: Please explain the standardised protocol for the sake of reproducibility.-This has now been added L145-149: was this performed in the same experimental batch as in the previous section? Either way, if the methodology is the same, the authors can refer to that section rather than repeating. -I am not sure I understand your question, because all of the samples were processed in Tanzania to find lactose fermenters and resistant strains, while in the second screening in Glasgow was to find E. coli specifically in the mentioned section. So I was highlighting what was done in Glasgow L153: Please explain the standardised protocol for the sake of reproducibility. -This has been added L167-173: why use this method rather than 16S rRNA sequencing? -We didn't do this for budgetary constraints L191: tigecycline belongs to the same antibiotic family as tetracycline, but it is not analogous to it. Please justify why the authors consider appropriate substituting one for the other.
Thank you for your valuable input. We have carefully reviewed your comments and have made the necessary adjustments to address the concern you raised. In the manuscript, we have included the following statement to justify our approach: "Since ECOFFs for 30g tetracycline were unavailable on the EUCAST website, the distribution of tigecycline, a member of the same antimicrobial class as tetracycline with the requisite concentration, was substituted for visual comparison purposes. The focus of our analysis was on comparing the distribution patterns between the current study and the reference distributions of the mentioned antimicrobial class. This allowed us to assess the similarity or dissimilarity between our data and the established reference distributions. It is important to note that the breakpoint for tigecycline was not utilized in this analysis to ensure accuracy and avoid potential misinterpretations." By incorporating this clarification in the manuscript, we aim to provide transparency regarding our choice of substitution and emphasize our commitment to accuracy in our analysis. We appreciate your guidance and believe that these amendments strengthen the scientific rigor of our study.
L207: Are the different isolates individually labelled/barcoded? As they are mentioned, the reader cannot know which of them correspond to each result. This information must be made available. -This will be available as ESI alongside the manuscript.
L211: Figure 1 is not cited in the text. Furthermore, it merely shows a calibration curve. It would much more appropriate to show the actual qPCR results compared to a negative and positive control.-I have removed this image as suggested.
L215-216: This is a clear example of the need to improve clarity. The sentence "In at least one antimicrobial, there was one isolate with a 6 mm zone diameter". These sentence does not offer any information and, as there is no individualised information of the isolates, the reader cannot be able to track it. Also, to the best of my knowledge, 6-7 mm is the usual size of the antibiotic discs used for disc diffusiojob seen assays. Does this mean this grew to the edge of the disc? -This has now been clarified. L334: hyperefflux and antibiotic resistance are widely known to be linked. There are many more recent works in the literature the authors can refer to give a more comprehensive view of this. We habve added this in the discussion with literature L334-338: the authors mention differences in the resistance prevalence in birds from pristine environments with respect to the results of this work, obtained from farm chickens, and justify it by methodological discrepancies. To me, it is very obvious that those two sample groups were not expected to give similar results, and that differences might be affected by the methodology, but I do not think that is the main cause of the discrepancies between these two particular studies.
I think you have misunderstood us in this section. Our intention in this section was to discuss the differences between ECOFF distributions in animals and humans. We aimed to emphasise that the distribution of resistance in animals is expected to differ from the distribution observed in human-derived isolates, since ECOFF distributions are predominantly derived from human data. We referenced a study here to highlight that there is evidence suggesting similarities between bird isolates collected from pristine environments and human isolates, contrary to what one might expect. We agree methodological discrepancies are not the only reason. The results section lacks clarity. What isolates are used in the susceptibility testing in Table 1 and Fig. 2? Does this refer to the E. coli positive control? If so please indicate. We have clarified in the text and added ESI that addresses this.
Line 215/216: "In at least one antimicrobial, there was one isolate with a 6mm zone diameter". What do the authors mean? According to Fig. 2 there is at least one isolate with 6mm zone for each of the 4 tested antimicrobials. -This has now been clarified.
Line 216/217: "Except for tetracycline, the wild type cut-off (COWT) was lower than CB and ECOFFs". Where can I see this?
Or if not visualized, please put between brackets the actual CB and ECOFFS values. This has now been clarified in the text and is visible in figure 2. Table 1: please indicate how the functional peak was determined. Also clarify the following in Table legend as this is not currently understandable "….in a range of inhibition zone diameters and output by Normalised Resistance Interpretation (COWT)". We have p[provided reference to the universal protocol by Kronvall where all the details related to creation of functional peaks are explain including the online spreadsheet that is used in generating the breakpoint. We have also included explanation in the introduction about the functional peak.  The main findings are that ECOFFS breakpoints tend to be higher than clinical breakpoints or the relatively new method of NRI. Also, the authors observe unexpected prevalence of imipenem and ceftazidime resistance in poultry isolates, which they speculate may be related to resistance genes present in the local water reservoirs. Overall, the manuscript is straightforward, well written and easy to understand. However, in some cases the Results section lacks clarity and the Methodology lacks detail -to address these issues I have a list of minor corrections below that the authors should address before publication. Line 86: remove 'the' before Europe Line 117: what is meant by 'wards' here? I only know this term in a hospital context so please amendments could be made, but the changes proposed are extensive enough to leave the small details for a future corrected version. Please see the following specific comments. Specific comments L78: in microbiology, usually a wild type is a strain as it is found in nature, no matter what phenotype or genotype it has. Here, this concept is different, but would only be explained several lines below. I would tackle this distinction at this point, or else it might confuse some readers. L82-85: this sentence seems to imply that the EUCAST standards do not have clinical applicability at all. More clarity is needed here. L86: change "the Europe" by "Europe". L89: At this point, please give a clear explanation of how the NRI method works and its applicability. As it is explained, it seems very limited and the results might not be comparable to other samples/laboratories/regions. L90: here the authors mention a distinction between WT and NWT. However, this definition is still different from the general use of this expression in microbiology. Please clarify. L105: change "current" to "present". L118: where in Glasgow and which analyses where performed there? L123: I assume "Caudell" refers to reference 15, however this is not in the reference list. L125: if coliforms were assessed as resistant according to CLSI standards, would this not be biased according to this work? I suggest that the authors give a justification for this or at least a reflection. L133: Please clarify where each experiment was performed. L137: Please explain the standardised protocol for the sake of reproducibility. L145-149: was this performed in the same experimental batch as in the previous section? Either way, if the methodology is the same, the authors can refer to that section rather than repeating. L153: Please explain the standardised protocol for the sake of reproducibility. L167-173: why use this method rather than 16S rRNA sequencing? L191: tigecycline belongs to the same antibiotic family as tetracycline, but it is not analogous to it. Please justify why the authors consider appropriate substituting one for the other. L207: Are the different isolates individually labelled/ barcoded? As they are mentioned, the reader cannot know which of them correspond to each result. This information must be made available. L211: Figure 1 is not cited in the text. Furthermore, it merely shows a calibration curve. It would much more appropriate to show the actual qPCR results compared to a negative and positive control. L215-216: This is a clear example of the need to improve clarity. The sentence "In at least one antimicrobial, there was one isolate with a 6 mm zone diameter". These sentence does not offer any information and, as there is no individualised information of the isolates, the reader cannot be able to track it. Also, to the best of my knowledge, 6-7 mm is the usual size of the antibiotic discs used for disc diffusion assays. Does this mean this grew to the edge of the disc? L220: does this issue with the SD mean the results are not reliable? L225: what is the functional peak? L237: how would this compare to the CLSI breakpoints? L251: what is the prevalence estimate for cipro? L253: previously, the authors had mentioned that they would consider breakpoints for tigecycline as they are not available for tetracycline. However, here the authors consider all breakpoints except the one for tetracycline as it is not available. Please clarify this. L272: here ECOFFS should be CLSI breakpoint? L285-286: here the authors reach the idea that NRI interpretation might be affected by the dataset. Can the authors elaborate on how the application of this method can contribute to standardisation? L294-295: speaking of prevalence, the authors consider it high, low or moderate with respect to what? L323: I have my doubts that imipenem can be used informally. As a last resort antibiotic, not only it is restricted, but also rather expensive in comparison to other antibiotics. Anyway, can the authors give a reason why they think this antibiotic is informally used? Would there be any alternative explanation? L327-330: this sentence confuses me. According to my interpretation of Figure 3, all CB and COWT values are below the ECOFFS threshold, making them more restrictive in the definition of a resistant isolate. Can the authors clarify how those isolates could be misclassified as resistant according to ECOFFS in comparison to the other standards? L332-333: The authors should elaborate more on how the differences between the human and ruminant digestive tract relate to this study. L334: hyperefflux and antibiotic resistance are widely known to be linked. There are many more recent works in the literature the authors can refer to give a more comprehensive view of this. L334-338: the authors mention differences in the resistance prevalence in birds from pristine environments with respect to the results of this work, obtained from farm chickens, and justify it by methodological discrepancies. To me, it is very obvious that those two sample groups were not expected to give similar results, and that differences might be affected by the methodology, but I do not think that is the main cause of the discrepancies between these two particular studies.

Please rate the manuscript for methodological rigour Poor
Please rate the quality of the presentation and structure of the manuscript

Very poor
To what extent are the conclusions supported by the data? Partially support

Do you have any concerns of possible image manipulation, plagiarism or any other unethical practices? No
Is there a potential financial or other conflict of interest between yourself and the author(s)? No